Thesis Abstract

Abstract
The insurance industry is increasingly turning to artificial intelligence (AI) technologies to enhance fraud detection capabilities and improve operational efficiency. This thesis investigates the impact of AI on fraud detection in insurance companies. The study explores how AI tools such as machine learning algorithms and natural language processing can be leveraged to detect fraudulent activities more effectively and efficiently. The research examines the current state of fraud detection practices in the insurance sector, identifies the challenges and limitations faced by traditional methods, and evaluates the potential benefits of integrating AI solutions. The literature review in this thesis provides a comprehensive overview of existing research on fraud detection, AI technologies, and their applications in the insurance industry. It discusses key concepts, theories, and models related to fraud detection and AI, highlighting the strengths and limitations of different approaches. The review also examines case studies and empirical studies that demonstrate the effectiveness of AI in improving fraud detection accuracy and reducing false positives. The research methodology section outlines the approach taken to investigate the impact of AI on fraud detection in insurance companies. The study employs a mixed-methods research design, combining quantitative analysis of historical fraud data with qualitative interviews and surveys of industry experts. Data collection methods include structured surveys, in-depth interviews, and analysis of historical fraud cases to identify patterns and trends. The findings of this study reveal that AI technologies have the potential to significantly enhance fraud detection capabilities in insurance companies. Machine learning algorithms can analyze large volumes of data in real-time, identify suspicious patterns, and flag potential fraud cases with high accuracy. Natural language processing tools can extract relevant information from unstructured data sources such as text documents and emails, enabling more effective fraud detection and investigation. The discussion section provides a detailed analysis of the research findings, highlighting the implications for insurance companies and the broader industry. It explores the challenges and opportunities associated with adopting AI technologies for fraud detection, including issues related to data privacy, regulatory compliance, and ethical considerations. The study also discusses the implications for fraud prevention strategies, risk management practices, and operational processes within insurance companies. In conclusion, this thesis underscores the transformative potential of AI in enhancing fraud detection capabilities in insurance companies. By leveraging advanced technologies such as machine learning and natural language processing, insurers can improve their ability to detect and prevent fraudulent activities, protect policyholders, and minimize financial losses. The study also identifies key areas for future research and recommends best practices for implementing AI-driven fraud detection solutions in the insurance industry.

Thesis Overview